A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mutations only need to be useful about 1% of the time for the search to keep making progress. Akarsh is a PhD student at MIT working with Phillip Isola, works with Sakana AI, and is first author of the Fractured Entangled Representation paper with Kenneth Stanley, Jeff Clune and Joel Lehman. He tells Tim Scarfe why the path a learner takes may shape the structure of what it learns, and why that is a different way of looking at intelligence from the statistical one. Most of the conversation is about ASAL, the method he led for searching whole spaces of artificial worlds. Rather than predict what a rule will do, ASAL runs the simulation and asks a foundation model what happened. Mapped across all 262,144 Life-like rules, the most interesting worlds sit on one small island. Along the way: Game of Life, Lenia (with a clip from its creator, Bert Chan), neural cellular automata, Boids, Particle Life and the emergence of persistence. The last stretch is Core War, a programming game from the 1980s. Asked cold, LLMs write poor Redcode. Used as the mutation step in an evolutionary loop with a verifier, they produced warriors that, between them, beat or tied 96% of 294 human-written ones. Over repeated rounds the warriors trended towards generalists. --- --- REFERENCES: paper: [00:04:19] ASAL: Automating the Search for Artificial Life with Foundation Models (Kumar et al.) https://arxiv.org/abs/2412.17799 [00:08:08] Assembly theory (Sharma et al., Nature 2023) https://www.nature.com/articles/s41586-023-06600-9 [00:09:54] The Fractured Entangled Representation Hypothesis (Kumar, Clune, Lehman, Stanley) https://arxiv.org/abs/2505.11581 [00:22:38] Lenia: Biology of Artificial Life (Bert Chan) https://arxiv.org/abs/1812.05433 [00:23:20] Growing Neural Cellular Automata (Mordvintsev et al.) https://distill.pub/2020/growing-ca/ [00:39:49] CLIP (Radford et al.) https://arxiv.org/abs/2103.00020 [00:41:35] Digital Red Queen: Core War with LLMs (Kumar et al.) https://arxiv.org/abs/2601.03335 [00:43:42] MAP-Elites (Mouret and Clune) https://arxiv.org/abs/1504.04909 [00:44:55] AlphaEvolve (Google DeepMind) https://arxiv.org/abs/2506.13131 [00:47:03] AutoML-Zero (Real et al.) https://arxiv.org/abs/2003.03384 book: [00:07:04] Why Greatness Cannot Be Planned (Stanley and Lehman) https://link.springer.com/book/10.1007/978-3-319-15524-1 tool: [00:18:12] Conway's Game of Life https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life [00:25:27] Boids (Craig Reynolds) https://www.red3d.com/cwr/boids/ [00:27:12] Particle Life (Tom Mohr) https://github.com/tom-mohr/particle-life [00:41:50] Core War https://corewar.co.uk/ other: [00:08:49] Computational irreducibility (Stephen Wolfram) https://www.wolframscience.com/nks/p737--computational-irreducibility/ [00:10:46] MLST: Why Every AI Model Is an Impostor (Kenneth Stanley, FER documentary) https://www.youtube.com/watch?v=o1q6Hhz0MAg [00:28:30] MLST: Blaise Agüera y Arcas on life emerging from code https://www.youtube.com/watch?v=rMSEqJ_4EBk [00:33:35] Goodhart's law https://en.wikipedia.org/wiki/Goodhart%27s_law --- LINKS: Akarsh Kumar: https://akarshkumar.com/ ASAL project page and demos: https://pub.sakana.ai/asal/ Digital Red Queen project page: https://sakana.ai/drq/ RESCRIPT: https://app.rescript.info/public/share/Qfv3T0EVzqOXL_CYYeRr9Blv8HnNm4JsXBh7ByFjJbc